Individuals with chronic brain injuries, including concussion, reliably exhibit decreased ERN and Pe amplitudes, which are associated with impairments in error detection and diminished error- based learning. However, no prior study has compared the ERN and Pe in asymptomatic and symptomatic concussed individuals to determine if the ERN and Pe can serve as biomarkers of recovery from brain injury. The Go/No-Go task (see Design) produces frequent response errors and is sensitive to concussive brain injuries. Accordingly, our first aim is to examine the ERN and Pe during a standard, discrete version of the Go/No-Go task to establish whether ERN and Pe can be used as physiological biomarkers of concussion recovery. This will also allow us to create normative standards of ERN and Pe amplitudes that can be directly compared to ERN and Pe amplitudes obtained from a continuous task performed using the KINARM robot. Upper-limb robots, such as the KINARM, are well suited for assessing and rehabilitating neurological deficits caused by brain injuries. They permit objective, reliable quantification of perceptual, motor and cognitive function and allow high-repetition practice using continuous tasks that capture real-world behavioral dynamics. Furthermore, robotic tasks can be altered to modify perceptual, cognitive, and motor requirements. However, current paradigms used to study ERN and Pe employ discrete tasks that impede the generalization of ERN and Pe findings to continuous tasks. Accordingly, our second aim will be to validate the use of a virtual-robotic environment (KINARM) to elicit the ERN and Pe in a realistic continuous performance environment. Findings from this project will provide researchers and clinicians with a powerful method to identify those with abnormal neurocognitive profiles, and provide researchers with an ecologically valid environment to assess and rehabilitate concussed individuals.